Comparison of two bias correction methods for precipitation simulated with a regional climate model

Comparison of two bias correction methods for precipitation simulated with a regional climate model

This study evaluates the performance of two bias correction techniques—power transformation and gamma distribution adjustment—for Eta regional climate model (RCM) precipitation simulations. For the gamma distribution adjustment, the number of dry days is not taken as a fixed parameter; rather, we pr...

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Bibliographic Details
Published in:Theoretical and applied climatology Vol. 127; no. 3-4; pp. 841 - 852
Main Authors: Tschöke, Gabriele Vanessa, Kruk, Nadiane Smaha, de Queiroz, Paulo Ivo Braga, Chou, Sin Chan, de Sousa Junior, Wilson Cabral
Format: Journal Article
Language:English
Published: Vienna Springer Vienna 01-02-2017
Springer
Springer Nature B.V
Subjects:
Analysis
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Climate models
Climate science
Climatology
Earth and Environmental Science
Earth Sciences
Greenhouse gases
Hydrologic cycle
Methods
Original Paper
Precipitation (Meteorology)
Studies
Waste Water Technology
Water Management
Water Pollution Control
Power Transformation
Bias Correction
Cumulative Distribution Function
Gamma Distribution
Regional Climate Model
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Summary:This study evaluates the performance of two bias correction techniques—power transformation and gamma distribution adjustment—for Eta regional climate model (RCM) precipitation simulations. For the gamma distribution adjustment, the number of dry days is not taken as a fixed parameter; rather, we propose a new methodology for handling dry days. We consider two cases: the first case is defined as having a greater number of simulated dry days than the observed number, and the second case is defined as the opposite. The present climate period was divided into calibration and validation sets. We evaluate the results of the two bias correction techniques using the Kolmogorov-Smirnov nonparametric test and the sum of the differences between the cumulative distribution curves. These tests show that both correction techniques were effective in reducing errors and consequently improving the reliability of the simulations. However, the gamma distribution correction method proved to be more efficient, particularly in reducing the error in the number of dry days.
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ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-015-1671-z